AI Agents Forget.
HippocampAI Remembers.
Production-ready memory layer with knowledge graphs, hybrid retrieval, multi-agent collaboration, and 102+ API methods. Give your AI systems human-like memory capabilities.
Get started in 3 steps
Add long-term memory to your AI application in minutes
Install
Get started with pip
pip install
hippocampaiInitialize
Create a memory client
from hippocampai
import MemoryClient
client = MemoryClient()Remember
Store and recall
client.remember(
"Your first memory",
user_id="alice"
)How HippocampAI Works
A robust pipeline designed for reliability and performance at scale
Memory Types
- Preferences
- Facts & knowledge
- Events & conversations
- Procedural (behavioral)
- Custom metadata
Processing
- Multi-provider embeddings
- Knowledge graph extraction
- Importance scoring
- Relevance feedback loops
Storage & Caching
- Qdrant vector DB
- Redis caching (50-100x)
- Tiered storage
- Export/import
Sleep Phase
- Memory consolidation
- Importance decay
- Auto-healing
- Predictive analytics
Everything you need for AI memory
From knowledge graphs to multi-agent collaboration — all the primitives for building memory-enabled AI applications
Knowledge Graph
Real-time entity and relationship extraction on every remember() call. Build structured knowledge automatically from unstructured text.
3-way RRF fusion: vector + BM25 + graph retrieval
Hybrid Retrieval
Combines semantic search, BM25 keyword matching, and graph-aware retrieval with Reciprocal Rank Fusion for optimal results.
40% accuracy improvement over vector-only search
Multi-Agent Collaboration
Shared memory spaces for agent coordination. Multiple AI agents can read, write, and reason over collective memories.
Agent-scoped access with shared namespaces
Sleep Phase Consolidation
Bio-inspired memory consolidation merges related memories, decays importance scores, and prunes low-value data automatically.
Inspired by human memory consolidation during sleep
Predictive Analytics
Pattern detection, habit tracking, and behavioral insights. Predict memory usage patterns and forecast future needs.
Cross-session analytics with temporal reasoning
Auto-Healing
Automatic detection and repair of memory issues. Health monitoring, duplicate detection, and quality tracking built in.
Self-monitoring with configurable health thresholds
Memory Triggers
Event-driven actions via webhooks, websocket, and log triggers. React to memory events in real-time across your system.
Configurable trigger rules with filter conditions
Procedural Memory
Self-optimizing prompts via learned behavioral rules. Your AI learns how to communicate better over time.
Feedback loops with exponential decay scoring
Plugin System
Extensible architecture with custom processors, scorers, retrievers, and filters. Build exactly the memory system you need.
Custom schemas, tiered storage, export/import
SaaS Platform
Full multi-tenant platform with authentication, rate limiting, Celery background tasks, and a React dashboard.
pip install hippocampai[saas] for full platform
Importance Scoring
Not all memories are equal. Automatic importance scoring with access-based boosting and configurable decay rates.
Time-weighted scoring with relevance feedback
Memory Namespaces
Hierarchical organization with permissions. Isolate memory spaces per user, session, team, or custom scope.
Bi-temporal facts with time-travel queries
Simple API, powerful features
Get started in minutes with our intuitive Python SDK. 102+ methods covering memory storage, retrieval, knowledge graphs, multi-agent coordination, and more.
pip install hippocampaiPyPIfrom hippocampai import MemoryClient
# Initialize client
client = MemoryClient()
# Store a memory
client.remember(
"User prefers oat milk and works remotely on Tuesdays",
user_id="alice",
type="preference"
)
# Recall relevant memories
results = client.recall("work preferences", user_id="alice")
for r in results:
print(f"{r.memory.text} (relevance: {r.score:.2f}")Works with your stack
Seamlessly integrate with popular LLM providers, storage backends, and AI frameworks
LLM & Embedding Providers
Storage Backends
Frameworks & Platforms
How we compare
See how HippocampAI stacks up against other memory solutions
| Feature | HippocampAIOpen Source | Mem0Cloud | LangChainMemory | CustomSolution |
|---|---|---|---|---|
| Semantic Search | ||||
| Knowledge Graph | ||||
| Graph-Aware Retrieval (3-way RRF) | ||||
| Auto-Deduplication | ||||
| Hybrid Retrieval (BM25 + Vector) | ||||
| Multi-Agent Collaboration | ||||
| Memory Triggers & Webhooks | ||||
| Procedural Memory | ||||
| Sleep Phase Consolidation | ||||
| Predictive Analytics | ||||
| Auto-Healing | ||||
| Self-Hosted Option | ||||
| Built-in Dashboard UI | ||||
| Plugin System | ||||
| Open Source (Apache 2.0) |
Built for every use case
HippocampAI powers memory for diverse AI applications across industries
Conversational AI
Build chatbots that remember user preferences, conversation history, and context across sessions.
AI Agents
Create autonomous agents that learn from interactions and make informed decisions based on past experiences.
Document Q&A
Enable semantic search over large document collections with context-aware retrieval.
Multi-User Systems
Manage separate memory spaces for different users while maintaining privacy and isolation.
Personalization
Deliver tailored content and suggestions by learning from user behavior and preferences.
Performance that scales
Built for production workloads with enterprise-grade reliability
Open source & community driven
Join the community building the future of AI memory
Frequently asked questions
Everything you need to know about HippocampAI
HippocampAI is an open-source, production-ready memory engine that gives AI systems human-like memory capabilities. Named after the brain region responsible for memory formation, it provides persistent memory storage, knowledge graphs, hybrid retrieval, multi-agent collaboration, and 102+ API methods for building memory-enabled AI applications.
Still have questions?
Ask on GitHub DiscussionsChangelog & Roadmap
Stay up to date with the latest features and improvements
Recent Releases
- Knowledge graph with real-time entity extraction
- Graph-aware retrieval (vector + BM25 + graph RRF)
- Relevance feedback loop with decay scoring
- Memory triggers (webhooks, websocket, log actions)
- Procedural memory & prompt self-optimization
- Embedding model migration with Celery
- Multi-agent collaboration with shared memory
- Predictive analytics & pattern forecasting
- Auto-healing memory system
- React dashboard with full analytics UI
- Plugin system (processors, scorers, retrievers)
- Memory namespaces with permissions
- SaaS platform (auth, rate limiting, Celery)
- Export/import (JSON, Parquet, CSV)
- Tiered storage (hot/warm/cold)
- Offline mode with operation queueing
- Bi-temporal facts with time-travel queries
- Context assembly with token budgeting
- Production-ready memory engine
- Sleep Phase memory consolidation
- Hybrid retrieval (BM25 + vector)
- Multi-user & session support
Roadmap
Ready to get started?
HippocampAI is open source and free to use. Star us on GitHub to show your support and stay updated with new releases.
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